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ETL vs ELT Key Differences

ETL vs ELT Simplified: Discover how these data processing techniques differ and which one can revolutionize the way your business handles data.

By

Jatin Solanki

Updated on

June 15, 2024

ETL vs ELT Key Differences

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In the subject matter of data warehousing, ETL and ELT are two extensively used procedures for data integration. ETL is an abbreviation for Extract, Transform, and Load, whereas ELT is an abbreviation for Extract, Load, and Transform. Both ETL and ELT involve moving data from source systems to a data warehouse, but they differ in the way they handle data transformation.

As per Forbes, More than 80% of corporate Business Operations leaders say that Data integration is crucial for business operations. Currently, 67% of firms count on data integration to support analytics and BI platforms, with 24% planning to do so within the next year. The increasing requirement for organizations to make data-driven decisions, as well as the expanding volume of data created by diverse sources, are driving such growth.

Quoting Bill Inmon, the father of data warehousing, defines "a Data Warehouse (DW) as "a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision-making process."

ETL involves extracting data from source systems, changing it into an analysis-ready format, and loading it into a data warehouse. This transformation can involve various activities, such as data cleaning, normalization, aggregation, and enrichment.

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ELT, on the other hand, entails extracting data from source systems, transferring it to a data warehouse, and then transforming it with SQL or other data manipulation tools. According to a report by TDWI Research, ELT is gaining popularity as it allows for more flexibility and scalability in data warehousing, enabling users to perform more complex transformations directly in the data warehouse.

We need to analyze these in more detail to make sense of both these processes. Let us do that in today's blog.

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What is ETL?

ETL, or Extract, Transform, Load, is a popular data integration approach that involves extracting data from various sources, transforming it to fit the target system, and then loading it into a centralized repository. This process involves three key stages: Extraction, Transformation, and Loading. ETL is an essential tool for processing huge volumes of data and ensuring its precision and consistency.

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The process:

Now let us talk about the process. Extraction involves pulling data from source systems such as databases, applications, and files. This can involve using tools such as SQL queries, data integration tools, or APIs.

Transformation involves converting and manipulating data into a format suitable for analysis which involves cleaning, aggregating, enriching data with additional information, or applying business rules to the data.

Loading involves transferring data into a target system, typically a data warehouse or data mart. This can involve using ETL software, SQL scripts, or other data-loading tools.

ETL is widely used in data warehousing as it allows for a structured and efficient way to integrate data from various sources into a centralized warehouse. This gives businesses a more detailed and factual view of their data, allowing for better decision-making.

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What is ELT?

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Now when we talk about ELT, ELT, or Extract, Load, Transform is another data integration approach that has recently gained popularity. ELT involves two key stages: Extraction, Loading, and Transformation. Unlike ETL, ELT involves loading data into the target system first and transforming it as needed. This process allows for greater flexibility in data processing and analysis, particularly with unstructured or semi-structured data. ELT can also provide faster processing times by avoiding the need for data transformation before loading. 

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The process:

In this process, Extraction involves pulling data from source systems, such as databases, applications, and files, similar to ETL.

Loading involves transferring data into a target system, typically a data warehouse or data mart, similar to ETL.

Transformation, however, is different in ELT. Instead of transforming data before loading it into the data warehouse, ELT loads it first and then transforms it using SQL or other data manipulation tools.

ELT allows for more flexibility and scalability in data warehousing, as it facilitates users to perform more complex transformations directly in the data warehouse. This can save time and reduce the need for other transformation processes, as the data is already in the warehouse.

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Key Differences Between ETL and ELT

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While both ETL and ELT are data integration approaches that enable businesses to combine data from multiple sources into a centralized warehouse, there are key differences between the two. These differences can have a significant impact on how businesses choose to approach data integration.

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  • One significant difference between ETL and ELT is the order in which data is processed. In ETL, data is extracted from various sources, transformed to fit the target system, and then loaded into centralized storage. In contrast, ELT involves loading data into the target system first and then transforming it as needed. This difference has significant implications for data processing and analysis.
  • Another key difference between ETL and ELT is the role of the target system. In ETL, the target system is critical in the data transformation process. The data must be transformed to fit the target system's schema and requirements before being loaded into the centralized repository. In contrast, ELT enables businesses to leverage the target system's processing power and storage capacity for data transformation rather than relying on external tools.
  • A third difference between ETL and ELT is the level of data processing complexity. ETL is often used for structured data processing, where the data is already organized and formatted in a predefined way. ELT, on the other hand, is often used for unstructured or semi-structured data processing, where the data may not be organized in a predefined way and requires more flexible data processing tools.
  • Finally, the cost can also be a key consideration when choosing between ETL and ELT. ETL tools can be more expensive than ELT tools, particularly when it comes to licensing and infrastructure costs. However, ETL can be more efficient and cost-effective when processing large volumes of structured data.

In summary, while ETL and ELT are effective data integration approaches, there are significant differences to consider when deciding which approach to use. Data processing complexity, target system capabilities, and cost can all impact the decision. Ultimately, the right approach will depend on the specific needs of the business and the types of data being processed.

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When selecting between ETL and ELT, it's essential to consider factors such as the type and volume of data being processed, as well as the tools and technologies available. What more? Read on;

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When to Use ETL vs. ELT: Choosing the Right Approach:

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While ETL and ELT are commonly used for data integration, there are specific systems where one approach may be more suitable. Let's zoom into some of the essential factors that can help you select which process to choose.

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1. Data Volume:

One important consideration when choosing between ETL and ELT is the volume of data that needs to be processed. ETL may be better suited for batch processing of large volumes of data, as it allows for more efficient processing and can help prevent overload on the target system. ELT, on the other hand, is a better fit for real-time processing and analysis of smaller data sets.

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2. Data Complexity:

The complexity of the data being processed can also influence the choice of approach. ETL may be more appropriate for structured data, such as relational databases, where data transformation can be more easily defined and automated. In contrast, ELT may be better suited for semi-structured and unstructured data, such as log files and social media feeds, where the data structure is less well-defined and requires more flexibility.

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3. Data Latency:

Another factor to consider is the desired data latency or the time it takes to be processed and made available for analysis. ETL may result in longer processing times due to the need for data transformation before loading into the target system. In comparison, ELT can provide faster processing times by loading the data first and then performing transformation in the target system.

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4. Cost:

Cost is also an essential factor to consider when choosing between ETL and ELT. ETL typically requires specialized ETL software, which can be expensive and complex to set up and maintain. ELT, on the other hand, can use more common tools such as SQL, which can be easier and more cost-effective to implement.

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So now you know which one to choose, rather than which one is appropriate for your business operations.

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Conclusion: Choosing the Right Data Integration Process for Your Business:

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In today's data-obsessed world, businesses of all sizes recognize the importance of integrating data from various sources into a centralized storehouse. ETL and ELT are two popular data integration approaches, each with strengths and weaknesses. While ETL is the more traditional approach, ELT offers greater flexibility and scalability.

When deciding between ETL and ELT, it's essential to consider factors such as data volume, complexity, desired data latency, and cost. By choosing the right approach for your business needs and following best practices for data integration, you can ensure successful implementation and gain valuable insights that can inform decision-making and drive growth.

Data integration is an ongoing process that requires careful planning and management, but the benefits are evident. By fetching together data from various sources, businesses can gain a holistic view of their processes, identify areas for improvement, and make data-driven decisions that help them stay ahead of the competition.

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In conclusion, whether you choose ETL or ELT, the key to successful data integration lies in careful planning, best practices, and ongoing management. Leveraging the might of data integration and making informed decisions based on priceless insights will help businesses not only survive but flourish in today's dynamic and ever-changing marketplace.

So are you ready to make a clear choice?

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External references:
  1. Forbes - "Why Data Integration Is Key To Business Operations":
  2. Gartner's Magic Quadrant for Data Integration Tools: https://www.gartner.com/en/documents/3986748/magic-quadrant-for-data-integration-tools‍

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What is a Data Trust Platform in financial services?
A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
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What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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